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Publications
Operationalising Life Cycle Assessment: Surrogate Models for Real-Time Decision-Making in Production
Kay Langhammer, Michael Ritthoff, George Margetis, Michail Vardakis
September 2026
Brightcon 2026, Aalborg, DK
Abstract
Life-cycle assessments are rarely applied as often to the operational decision-making in production as to the planning stage, despite the significant cumulative environmental impacts of high-volume processes. The bottleneck here is that an LCA is too complex for daily use, and decision-makers in production are not trained to interpret the results.
For the ENCIRCLE project, parameterised LCAs are conducted, using as parameters operational variables. The parameters are varied and used to train a surrogate model with the output, i.e. the life-cycle impacts. To determine the stopping criterion for the calculations, the CO2e footprint from the LCA calculations is compared with the optimisation potential derived from more accurate LCI results.
The approach is being tested in two industrial use cases: galvanising and aluminium recycling. As the surrogate model’s output is integrated into an Reinforcement Learning AI agent as an optimization objective, it must aggregate all impacts into a single normalized value to form its reward function, a requirement present both in the simulation environment where the agent trains, and the live production line in which it will imminently be deployed.
To this end, the values in the damage categories of ReCiPe 2016 are normalised so that a standard use case corresponds to 100%.
An interactive notebook is used to demonstrate how the surrogate model is generated and ported in Brightway 2.5, using the joblib package.
Links
Conference: https://indico.d-d-s.ch/event/2/
Playful Sorting: Assessing the Impact of Gamification on Organic Waste Separation through a Field Experiment and Sensor Data
Reinhard, Julia Beatrix; Kay Langhammer; Imke Schmidt
July 2026; 16th Conference of the European Society for Ecological Economics (ESEE); Ghent, Belgium
Abstract
Achieving a sustainable circular economy necessitates a rethinking of urban metabolism and resource recovery. While organic waste is a critical residual stream, its potential is often undermined by poor sorting quality and avoidable food waste at the household level. This research addresses these challenges by investigating digital gamification as a tool to activate citizens and overcome psychological barriers to consistent circular behavior. Applying the COMB model, we conducted a quasi-experimental field study in two diverse urban quarters using a gamified smart city application. The study integrates longitudinal survey data with objective behavioral measurements from sensor-equipped waste collection vehicles. Beyond sorting purity, we assess the impact of gamified engagement on knowledge, attitudes toward waste avoidance, and the role of neighborhood identification. The results offer guidance for municipal authorities on designing digital tools to foster long-term circular behavior and participation.
Links
Künstliche Intelligenz für die Kreislaufwirtschaft: Eine systematische Analyse von KI‑Anwendungen zur Unterstützung zirkulärer Strategien
Kay Langhammer, Daniel Wurm, Stephan Ramesohl, Justus von Geibler, Manuel W. Bickel, Holmer Hemsen
June 2026
Abstract
Artificial intelligence (AI) can support the transition to a circular economy (CE) at every stage of the value chain. This means AI can help to conserve resources, extend product lifespans and close material loops as far as possible. However, this can only be achieved if the resource savings are not outweighed by AI’s own resource consumption and if rebound effects are avoided – such as increased consumption resulting from new products or services on the market. This policy paper illustrates potential applications through practical examples and provides recommendations for the socially, economically and environmentally sustainable use of AI in seven key areas of the CE.
Links
DOI: 10.14512/OEW4102046
Interoperability Requirements for Digital Product Passports in the Circular Economy
Kay Langhammer, Niklas Hoffman, Maximilian Blum
May 2026
SETAC Europe 36ᵗʰ Annual Meeting, Maastricht, NL
Abstract
Access to reliable, interoperable life cycle data is essential for enabling transparent, reproducible and scalable sustainability assessments. As digital product information flows across organisations, sectors and tools, the ability of systems to exchange and interpret data consistently becomes a critical requirement for the emerging Digital Product Passport (DPP). This presentation examines the current landscape of interoperability standards and derives key technical, semantic, syntactic and organisational requirements that can support the development of cross-sectoral, machine-readable and trustworthy sustainability data infrastructures.
Künstliche Intelligenz für die Circular Economy – Ein Werkzeug für die nachhaltige Transformation?
Michael Leitl, Jan Quaing, Birgitt Helms, Kay Langhammer, Johanna Graf, David Rohrschneider, Paul Szabó-Müller
February 2025
Abstract
Artificial intelligence (AI) can support the transition to a circular economy (CE) at every stage of the value chain. This means AI can help to conserve resources, extend product lifespans and close material loops as far as possible. However, this can only be achieved if the resource savings are not outweighed by AI’s own resource consumption and if rebound effects are avoided – such as increased consumption resulting from new products or services on the market. This policy paper illustrates potential applications through practical examples and provides recommendations for the socially, economically and environmentally sustainable use of AI in seven key areas of the CE.
Links
Investigation of shape deviations of expanded and slitted tube ends
Kay Langhammer, Bernd Engel
July 2019
ESAFORM 2019, Bilbao, ES
Abstract
The expansion of tube ends is often realized by axial forming with a mandrel. Usually, the purpose of this forming is to allow the expanded tube end to be fitted onto another tube end. In order to ensure the tightness of this connection, the expanded tube end is slit so that it can be clamped. Residual stresses are released from the expansion process, among other things, resulting in shape deviations in the form of out-of-roundness. In this case, reworking must be carried out in order to enable joining. The required tightness must be maintained by all means if the tubes are used to conduct fluids. The aim of the research is therefore to minimize the residual stresses caused by expansion. For the suitability of the component, however, it is not the residual stress distribution itself that is important, but the resulting shape after trimming. For this reason, a FE simulation model was created which was used to compare the calculated residual stress distributions and the resulting shape deviations after cutting. The influences of qualitative and quantitative process parameters were analyzed, in particular those of the mandrel geometry. Among other things, different mandrel geometries were examined and optimized which effect a two-stage forming in one stroke. The simulation results are validated with a series of experiments.
Links
DOI: 10.1063/1.5112656
Algorithm for the quantitative description of freeform bend tubes produced by the three-roll-push-bending process
Sebastian Groth, Bernd Engel, Kay Langhammer,
January 2018
Production Engineering 12(3–4):517–524
Abstract
Three-roll-push-bending (TRPB) is an innovative and flexible freeform bending process for the manufacturing of tube geometries with a continuous distribution of curvature along the bending line. The shaping is done kinematically, which means that the bending line is defined solely by the defined movement of the bending tools. Measurements of the resulting curvature have shown a characteristic oscillating transition zone over an extensive length of the part until a constant curvature is reached. The quantitative description of the transition zone and typical bending characteristics, such as radius of curvature and bending angle, is only possible to a limited extent using the common measurement approach. This paper presents an algorithm, which allows the determination of typical bending characteristics and the quantitative description of transition zones along the curvature distribution. For specimen components the results provided by this method are compared to those provided by a common method for the examination of bending radii. The algorithm forms the basis for investigating the influence of machine, tool and semi-finished parameters on the transition zone at the bending section.